Web search activity data accurately predict population chronic disease risk in the USA
Nguyen, Thin, Tran, Truyen, Luo, Wei, Gupta, Sunil, Rana, Santu, Phung, Dinh, Nichols, Melanie, Millar, Lynne, Venkatesh, Svetha and Allender, Steve 2015, Web search activity data accurately predict population chronic disease risk in the USA, Journal of epidemiology and community health, vol. 69, no. 7, pp. 693-699, doi: 10.1136/jech-2014-204523.
Attached Files
Name
Description
MIMEType
Size
Downloads
Title
Web search activity data accurately predict population chronic disease risk in the USA
BACKGROUND: The WHO framework for non-communicable disease (NCD) describes risks and outcomes comprising the majority of the global burden of disease. These factors are complex and interact at biological, behavioural, environmental and policy levels presenting challenges for population monitoring and intervention evaluation. This paper explores the utility of machine learning methods applied to population-level web search activity behaviour as a proxy for chronic disease risk factors. METHODS: Web activity output for each element of the WHO's Causes of NCD framework was used as a basis for identifying relevant web search activity from 2004 to 2013 for the USA. Multiple linear regression models with regularisation were used to generate predictive algorithms, mapping web search activity to Centers for Disease Control and Prevention (CDC) measured risk factor/disease prevalence. Predictions for subsequent target years not included in the model derivation were tested against CDC data from population surveys using Pearson correlation and Spearman's r. RESULTS: For 2011 and 2012, predicted prevalence was very strongly correlated with measured risk data ranging from fruits and vegetables consumed (r=0.81; 95% CI 0.68 to 0.89) to alcohol consumption (r=0.96; 95% CI 0.93 to 0.98). Mean difference between predicted and measured differences by State ranged from 0.03 to 2.16. Spearman's r for state-wise predicted versus measured prevalence varied from 0.82 to 0.93. CONCLUSIONS: The high predictive validity of web search activity for NCD risk has potential to provide real-time information on population risk during policy implementation and other population-level NCD prevention efforts.
080109 Pattern Recognition and Data Mining 111706 Epidemiology 111711 Health Information Systems (incl Surveillance) 110201 Cardiology (incl Cardiovascular Diseases)
Socio Economic Objective
970108 Expanding Knowledge in the Information and Computing Sciences
Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.